Analyse multi-échelles par ondelettes complexes : application aux signaux électrophysiologiques intracrâniens chez les patients épileptiques

L’objectif principal de cette étude est la conception d’un environnement de calcul logiciel, visant à mettre en place des outils d’analyse, dédiés à la caractérisation des signaux d’électroencéphalogrammes (EEG) intracrâniens chez les patients souffrants d’épilepsie. Plus précisément, cette étude vise à discriminer les phases préictales (avant la crise) des phases interictales (entre les crises). Lors de l’étude des signaux EEG, on observe trois caractéristiques. La première est la présence d’un comportement en loi de puissance de la densité spectrale de puissance (DSP). Ce comportement de la DSP présenté dans les travaux de Yamaguchi (2003) pourrait s’expliquer par la présence d’invariance d’échelles dans le signal. La seconde est l’augmentation du nombre d’oscillations hautes fréquences (OHF) à l’approche d’une crise. Ce résultat présenté par Zijlmans et al. (2011) implique une modification de l’allure de la DSP à l’approche de la crise. La troisième est l’utilisation de signaux EEG intracrâniens qui permettent d’observer les hautes fréquences grâce à un bon rapport signal sur bruit (RSB). Ces trois caractéristiques permettent d’envisager l’étude des propriétés d’invariance d’échelles pour la discrimination des phase interictales et préictales. Une des contributions de ce mémoire a été de développer un estimateur des propriétés d’invariance d’échelles qui utilise les ondelettes complexes symétriques de Daubechies introduites par Lina et Mayrand (1993). Ces ondelettes améliorent les performances de l’estimateur ainsi que la précision temporelle lors du calcul ondelettes. Une autre contribution significative est l’application de l’estimateur aux signaux EEG intracrâniens en utilisant une méthodologie à fenêtre glissante basé sur le test d’hypothèse. L’utilisation de cette méthodologie sur des signaux EEG intracrâniens chez des patients épileptiques durant la phase interictale et préictale a confirmé la présence de propriétés d’invariance d’échelles. Cette application a aussi montré des variations significatives de ces propriétés lors du passage de la phase interictale à la phase préictale. Ces résultats ont conduit à une collaboration avec l’Institut Neurologique de Montréal dans le cadre de la prédiction des crises épileptiques. Les résultats issus de cette collaboration viennent corroborer le diagnostique établit par le neurologue et permettent une amélioration des performances de l’algorithme de prédiction

Advanced framework for epilepsy detection through image-based EEG signal analysis

BackgroundRecurrent and unpredictable seizures characterize epilepsy, a neurological disorder affecting millions worldwide. Epilepsy diagnosis is crucial for timely treatment and better outcomes. Electroencephalography (EEG) time-series data analysis is essential for epilepsy diagnosis and surveillance. Complex signal processing methods used in traditional EEG analysis are computationally demanding and difficult to generalize across patients. Researchers are using machine learning to improve epilepsy detection, particularly visual feature extraction from EEG time-series data.ObjectiveThis study examines the application of a Gramian Angular Summation Field (GASF) approach for the analysis of EEG signals. Additionally, it explores the utilization of image features, specifically the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) techniques, for the purpose of epilepsy detection in EEG data.MethodsThe proposed methodology encompasses the transformation of EEG signals into images based on GASF, followed by the extraction of features utilizing SIFT and ORB techniques, and ultimately, the selection of relevant features. A state-of-the-art machine learning classifier is employed to classify GASF images into two categories: normal EEG patterns and focal EEG patterns. Bern-Barcelona EEG recordings were used to test the proposed method.ResultsThis method classifies EEG signals with 96% accuracy using SIFT features and 94% using ORB features. The Random Forest (RF) classifier surpasses state-of-the-art approaches in precision, recall, F1-score, specificity, and Area Under Curve (AUC). The Receiver Operating Characteristic (ROC) curve shows that Random Forest outperforms Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers.SignificanceThe suggested method has many advantages over time-series EEG data analysis and machine learning classifiers used in epilepsy detection studies. A novel image-based preprocessing pipeline using GASF for robust image synthesis and SIFT and ORB for feature extraction is presented here. The study found that the suggested method can accurately discriminate between normal and focal EEG signals, improving patient outcomes through early and accurate epilepsy diagnosis

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédic

Modelling and analysis of amplitude, phase and synchrony in human brain activity patterns

The critical brain hypothesis provides a framework for viewing the human brain as a critical system, which may transmit information, reorganise itself and react to external stimuli efficiently. A critical system incorporates structures at a range of spatial and temporal scales, and may be associated with power law distributions of neuronal avalanches and power law scaling functions. In the temporal domain, the critical brain hypothesis is supported by a power law decay of the autocorrelation function of neurophysiological signals, which indicates the presence of long-range temporal correlations (LRTCs). LRTCs have been found to exist in the amplitude envelope of neurophysiological signals such as EEG, EMG and MEG, which reveal patterns of local synchronisation within neuronal pools. Synchronisation is an important tool for communication in the nervous system and can also exist between disparate regions of the nervous system. In this thesis, inter-regional synchronisation is characterised by the rate of change of phase difference between neurophysiological time series at different neuronal regions and investigated using the novel phase synchrony analysis method. The phase synchrony analysis method is shown to recover the DFA exponents in time series where these are known. The method indicates that LRTCs are present in the rate of change of phase difference between time series derived from classical models of criticality at critical parameters, and in particular the Ising model of ferromagnetism and the Kuramoto model of coupled oscillators. The method is also applied to the Cabral model, in which Kuramoto oscillators with natural frequencies close to those of cortical rhythms are embedded in a network based on brain connectivity. It is shown that LRTCs in the rate of change of phase difference are disrupted when the network properties of the system are reorganised. The presence of LRTCs is assessed using detrended fluctuation analysis (DFA), which assumes the linearity of a log-log plot of detrended fluctuation magnitude. In this thesis it is demonstrated that this assumption does not always hold, and a novel heuristic technique, ML-DFA, is introduced for validating DFA results. Finally, the phase synchrony analysis method is applied to EEG, EMG and MEG time series. The presence of LRTCs in the rate of change of phase difference between time series recorded from the left and right motor cortices are shown to exist during resting state, but to be disrupted by a finger tapping task. The findings of this thesis are interpreted in the light of the critical brain hypothesis, and shown to provide motivation for future research in this area

Multifractal analysis for cumulant-based epileptic seizure detection in eeg time series

International audienceMultifractal analysis allows us to study scale invariance and fluctuations of the pointwise regularity of time series. A theoretically well grounded multifractal formalism, based on wavelet leaders, was applied to electroencephalogra-phy (EEG) time series measured in healthy volunteers and epilepsy patients, provided by the University of Bonn. We show that the multifractal spectrum during a seizure indicates a lower global regularity when compared to non-seizure data and that multifractal features, combined with few baseline features, can be used to train a supervised learning algorithm to discriminate well above chance ictal (i.e. seizure) versus healthy and interictal epochs (97 %) and healthy controls versus patients (92 %)